face_recognition.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "face_recognition.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMgAbRwbyEZTJc7irS5zr8G",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/big_pie/blob/master/face_recognition.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XU2KyaX-I7j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Conv2D\n",
        "from tensorflow.keras.layers import MaxPooling2D\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import pandas as pd\n",
        "import math\n",
        "import os\n",
        "import sys\n",
        "import shutil\n",
        "import urllib.request\n",
        "import zipfile\n",
        "import smtplib\n",
        "from matplotlib import pyplot\n",
        "from matplotlib.image import imread\n",
        "from datetime import datetime\n",
        "from email.message import EmailMessage"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3aOu0eFa3r7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "from torchvision.utils import save_image\n",
        "import torch\n",
        "import torchvision\n",
        "\n",
        "import scipy.misc\n",
        "from PIL import Image\n",
        "\n",
        "# from google.colab import files\n",
        "# uploaded = files.upload()\n",
        "\n",
        "# import zipfile\n",
        "# import io\n",
        "# zf = zipfile.ZipFile(io.BytesIO(uploaded['KGES.zip']), \"r\")\n",
        "# zf.extractall()\n",
        "\n",
        "face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n",
        "# eye_casecade = cv2.CascadeClassifier('../haarcascades/haarcascade_eye.xml')\n",
        "\n",
        "folder = 'face_recognition/'\n",
        "folder2 = 'KGES/'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r50H_Qrq4avZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "for i in range(0, 1016):\n",
        "  filename = folder2 + '0 (' + str(i) + ').jpg'\n",
        "\n",
        "  img = cv2.imread(filename)\n",
        "  # cv2_imshow(img)\n",
        "  # print(i)\n",
        "\n",
        "  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "  faces = face_cascade.detectMultiScale(gray, 1.3,5)\n",
        "  for (x,y,w,h) in faces:\n",
        "      # cv2.rectangle(img, (x,y), (x+w, y+h), (255,0,0),2)\n",
        "      # cropped = img[y - int(h/4):y + h + int(h/4), x - int(w/4):x + w + int(w/4)]\n",
        "      cropped = img[y:y+h, x:x+w, :].astype('uint8')\n",
        "      # roi_gray = gray[y:y+h, x:x+w]\n",
        "      # roi_color = img[y:y+h, x:x+w]\n",
        "      # eyes = eye_casecade.detectMultiScale(roi_gray)\n",
        "      # for (ex, ey, ew, eh) in eyes:\n",
        "          # cv2.rectangle(roi_color, (ex,ey), (ex+ew, ey+eh),(0,255,0),2)\n",
        "\n",
        "  # print(cropped)\n",
        "\n",
        "  # cropped.to_csv('dami_face_1.jpg')\n",
        "  # files.download('dami_face_1.jpg')\n",
        "  # print(type(cropped))\n",
        "\n",
        "  # cropped = cropped.numpy() # TypeError: tensor or list of tensors expected, got <class 'numpy.ndarray'>\n",
        "  # save_image()\n",
        "  # save_image(cropped, 'dami_face_1.jpg')\n",
        "  save_filename = folder + '0 (' + str(i) + ').jpg'\n",
        "  im = Image.fromarray(cropped).convert('RGB')\n",
        "  # print(im)\n",
        "  # im = (np.float32(im), cv2.COLOR_BGR2RGB)\n",
        "  # im = cv2.cvtColor(cv2.UMat(im), cv2.COLOR_BGR2RGB)\n",
        "  # print('///////////////')\n",
        "  # print(im)\n",
        "  cropped = cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR)\n",
        "  im = Image.fromarray(cropped).convert('RGB')\n",
        "  im.save(save_filename)\n",
        "  # scipy.misc.toimage(cropped, cmin=0.0, cmax=...).save('dami_face_1.jpg')\n",
        "  # cv2.imshow('Image view', cropped)\n",
        "  # cv2_imshow(cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))\n",
        "  cv2.waitKey(0)\n",
        "  cv2.destroyAllWindows()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3Q_SkYUpvVY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(0, 974):\n",
        "  filename = folder2 + '1 (' + str(i) + ').jpg'\n",
        "\n",
        "  img = cv2.imread(filename)\n",
        "  # cv2_imshow(img)\n",
        "  # print(i)\n",
        "\n",
        "  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "  faces = face_cascade.detectMultiScale(gray, 1.3,5)\n",
        "  for (x,y,w,h) in faces:\n",
        "      cropped = img[y:y+h, x:x+w, :].astype('uint8')\n",
        "\n",
        "  save_filename = folder + '1 (' + str(i) + ').jpg'\n",
        "  im = Image.fromarray(cropped).convert('RGB')\n",
        "\n",
        "  cropped = cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR)\n",
        "  im = Image.fromarray(cropped).convert('RGB')\n",
        "  im.save(save_filename)\n",
        "  \n",
        "  cv2.waitKey(0)\n",
        "  cv2.destroyAllWindows()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDa0EXX9ZKMq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(0, 526):\n",
        "  filename = folder2 + '3 (' + str(i) + ').jpg'\n",
        "\n",
        "  img = cv2.imread(filename)\n",
        "  # cv2_imshow(img)\n",
        "  # print(i)\n",
        "\n",
        "  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "  faces = face_cascade.detectMultiScale(gray, 1.3,5)\n",
        "  for (x,y,w,h) in faces:\n",
        "      cropped = img[y:y+h, x:x+w, :].astype('uint8')\n",
        "\n",
        "  save_filename = folder + '3 (' + str(i) + ').jpg'\n",
        "  im = Image.fromarray(cropped).convert('RGB')\n",
        "\n",
        "  cropped = cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR)\n",
        "  im = Image.fromarray(cropped).convert('RGB')\n",
        "  im.save(save_filename)\n",
        "  \n",
        "  cv2.waitKey(0)\n",
        "  cv2.destroyAllWindows()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V_fhBYj-ms-K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(0, 1000):\n",
        "  if i == 0 or i == 998:\n",
        "    filename = folder2 + '2 (' + str(i) + ').jpeg'\n",
        "  elif i == 1 or i == 2 or i == 3 or i == 994 or i == 995:\n",
        "    filename = folder2 + '2 (' + str(i) + ').png'\n",
        "  else:\n",
        "    filename = folder2 + '2 (' + str(i) + ').jpg'\n",
        "\n",
        "  img = cv2.imread(filename)\n",
        "  # cv2_imshow(img)\n",
        "  # print(i)\n",
        "\n",
        "  gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "  faces = face_cascade.detectMultiScale(gray, 1.3,5)\n",
        "  for (x,y,w,h) in faces:\n",
        "      cropped = img[y:y+h, x:x+w, :].astype('uint8')\n",
        "\n",
        "  if i == 0 or i == 998:\n",
        "    save_filename = folder + '2 (' + str(i) + ').jpeg'\n",
        "  elif i == 1 or i == 2 or i == 3 or i == 994 or i == 995:\n",
        "    save_filename = folder + '2 (' + str(i) + ').png'\n",
        "  else:\n",
        "    save_filename = folder + '2 (' + str(i) + ').jpg'\n",
        "\n",
        "  im = Image.fromarray(cropped).convert('RGB')\n",
        "\n",
        "  cropped = cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR)\n",
        "  im = Image.fromarray(cropped).convert('RGB')\n",
        "  im.save(save_filename)\n",
        "  \n",
        "  cv2.waitKey(0)\n",
        "  cv2.destroyAllWindows()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JA4D1tKEqD_c",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8bb25d57-c577-4689-9607-c4d002fcbd35"
      },
      "source": [
        "!zip -r /content/file.zip /content/face_recognition"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUPUh5OyqE9h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import files\n",
        "files.download(\"/content/file.zip\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pBtDU1GqkBk1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# # load dogs vs cats dataset, reshape and save to a new file\n",
        "# from os import listdir\n",
        "# from numpy import asarray\n",
        "# from numpy import save\n",
        "# from keras.preprocessing.image import load_img\n",
        "# from keras.preprocessing.image import img_to_array\n",
        "# # define location of dataset\n",
        "# folder = 'train/train/'\n",
        "# photos, labels = list(), list()\n",
        "# # enumerate files in the directory\n",
        "# for file in listdir(folder):\n",
        "# \t# determine class\n",
        "# \toutput = 0.0                #안지영(0 ~ 1015) total : 1016장\n",
        "# \tif file.startswith('1'):    #김고은(0 ~ 973) total : 974장\n",
        "# \t\toutput = 1.0\n",
        "#   elif file.startswith('2'):  #박소담(0 ~ 999) total : 1000장\n",
        "# \t\toutput = 2.0\n",
        "#   elif file.startswith('3'):  #김다미(0 ~ 525) total : 526장\n",
        "# \t\toutput = 3.0\n",
        "# \t# load image\n",
        "# \tphoto = load_img(folder + file, target_size=(200, 200))\n",
        "# \t# convert to numpy array\n",
        "# \tphoto = img_to_array(photo)\n",
        "# \t# store\n",
        "# \tphotos.append(photo)\n",
        "# \tlabels.append(output)\n",
        "# # convert to a numpy arrays\n",
        "# photos = asarray(photos)\n",
        "# labels = asarray(labels)\n",
        "# print(photos.shape, labels.shape)\n",
        "# # save the reshaped photos\n",
        "# save('kges_photos.npy', photos)\n",
        "# save('kges_labels.npy', labels)"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}